1. Field of the Invention
The present invention relates to an image matching apparatus which detects corresponding points in a source image and a target image to associate the same with each other, a method of matching images, and computer program product therefor.
2. Description of the Related Art
Image matching is a technique for detecting corresponding points of respective pixels in one source image (starting image) from another destination image (ending image) to find correspondence therebetween. The image matching techniques are utilized in various image processing such as motion detection of moving pictures, stereo matching, image morphing, image recognition, and moving picture encoding.
The image matching techniques can be classified into four main types, i.e., optical flow methods, block-based methods, gradient methods, and Bayesian methods, as disclosed in A. Murat Tekalp, “Digital Video Processing,” Prentice Hall, 1995.
According to the optical flow methods, an optical flow equation is derived based on an assumption of “constant luminance change,” to find the optical flow based on the optical flow equation as a constraint condition. On the other hand, according to the block-based methods, the image is divided into predetermined blocks, and motion is detected according to a template matching for each block. According to the gradient methods, matching is performed in a direction of descent of the luminance gradient in the image. In the Bayesian methods, matching is performed according to probabilistic likelihood.
Another highly robust conventional image matching technique is disclosed in Japanese Patent Publication No. 2927350, where plural multiresolution image pyramids are generated by plural multiresolution filters, and the matching is performed sequentially from an upper layer to a lower layer of the generated image pyramids, thereby allowing for the association of various motions ranging from rough motion to subtle motion of the images.
The conventional image matching techniques, however, are not immune to problems. The optical flow methods, being sensitive to noises by nature, have difficulties in dealing with fast motion. The block-based methods which perform the image matching for each block in the image, though presenting a high reliability in processing of motions such as horizontal translation of an object in the image, basically cannot maintain such a quality when the object in the image is, for example, transformed or rotated. The gradient methods which perform the matching in the direction of descent of the luminance gradient of the image have difficulties in constantly searching the motion of the object. Further, in the Bayesian methods, a global optimal point cannot be determined with ease.
In addition, the technique disclosed in Japanese Patent Publication No. 2927350 essentially requires plural multiresolution filters for the matching of the uppermost layer down to the lowermost layer of the multiresolution image pyramids, which entails difficulties in reduction of computational cost and structural expansion.
Thus, according to the conventional image matching techniques, the reliability of the method can be compromised depending on the presence of noise and motion of the image object. In addition, the improvement in the reliability of the image matching technique inevitably accompanies a further complication of the structure and the process, and ends up in limited expandability.